Systems and methods for attributing tv conversions

ABSTRACT

An attribution system aggregates and merges online data and offline chronologically. The attribution system examines merged data for unique visitor (UV) sessions initiated at an online medium (e.g., a website) within an attribution window for a spot that aired on an offline medium (e.g., a television network) and, for each conversion event that occurred in a UV session, assigns a session timestamp to it so that the conversion event is correlated to the spot. The attribution system then determines an overall conversion rate of UVs to the online medium in the attribution window and the attribution by the spot that aired on the offline medium to the overall conversion rate of UVs to the online medium in the attribution window. Results of the offline attribution to the online conversions can be visualized and presented on a client device communicatively connected to the attribution system.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of, and claims a benefit of priorityunder 35 U.S.C. § 120 from the filing date of U.S. patent applicationSer. No. 16/365,448, filed on Mar. 26, 2019, entitled “SYSTEMS ANDMETHODS FOR ATTRIBUTING TV CONVERSIONS,” which claims a benefit ofpriority under 35 U.S.C. § 119(e) from the filing date of U.S.Provisional Application No. 62/647,964, filed on Mar. 26, 2018, entitled“SYSTEMS AND METHODS FOR ATTRIBUTING TV CONVERSIONS,” which are fullyincorporated by reference herein for all purposes.

TECHNICAL FIELD

This disclosure relates generally to correlated data processing. Moreparticularly, this disclosure relates to systems, methods, and computerprogram products for correlated data processing of online media data andoffline media data, useful in bridging a data gap in attributing TVconversions.

BACKGROUND OF THE RELATED ART

Traditionally, television (TV) networks rely on a ratings system knownas the Nielsen ratings provided by Nielsen Media Research, headquarteredin New York, N.Y., U.S.A., to determine the audience size andcomposition of television programming in the United States. The Nielsenratings are gathered in one of two ways—using viewer diaries or setmeters attached to TVs in selected homes. The former requires a targetaudience self-record their viewing habits. The latter requires a specialdevice to collect specific viewing habits on a minute to minute basisand send the collected information to the Nielsen's ratings system overa phone line.

With the advent of the Internet, many aspects of modern life are nowdigitally connected through the seemingly ubiquitous smart phones, smartTVs, smart home appliances, Internet of Things (IoT) devices, websites,mobile apps, etc. Even so, many more analog aspects remain disconnectedfrom this digital world. Linear TV is an example of an offline mediumthat is disconnected from the digital world.

“Linear TV” refers to real time (live) television services that transmitTV program schedules. Almost all broadcast TV services can be consideredas linear TV. Non-linear TV covers streamlining and on-demandprogramming, which can be viewed at any time and is not constrained byreal-time broadcast schedules. Video-on-demand (VOD) and nearvideo-on-demand (NVOD) transmissions of pay-per-view programs overchannel feeds are examples of non-linear TV.

Because linear TV is an offline medium, it is not possible toautomatically collect information on viewers of linear TV. Accordingly,the Nielsen ratings system still plays an important role in how today'sTV networks determine the value of their television shows. However,while the Nielsen's ratings system can provide some quantified measuresof audience response to TV programs, the Nielsen ratings do not measureconversion rates for TV commercials.

SUMMARY OF THE DISCLOSURE

Embodiments disclosed herein provide new systems and methods forattributing conversion events originating from spots aired on linear TV.In this disclosure, “spots” refers to media creatives that aired oncertain TV networks (hereinafter “networks”) at certain timeslots and/orgeographical locations. A creative (which refers to content created fora particular purpose or campaign) can have multiple spots that aired ondifferent networks, at different times, under different cost structures.

In some embodiment, a method for attributing online conversion eventsoriginating from offline events such as spots airing on networks can beimplemented on an attribution system having an attribution analyzer, avisualizer, and a data store. The method can include aggregating onlinedata (e.g., clickstream data from a website) and offline data (e.g.,spot airing data from a network), merging the online data and theoffline data chronologically, and examining data from the merging forunique visitor (UV) sessions initiated at an online medium (e.g., thewebsite) within an attribution window of time for a spot that aired onan offline medium (e.g., the network). In some embodiments, thisexamination can include determining, for each UV session of the UVsessions, whether a conversion event occurred at the online medium inthe each UV session and, responsive to the conversion event occurring atthe online medium in the each UV session, assigning a session timestampto the conversion event. This session stamp indicates a start of theparticular UV session. The assignment of this session timestamp to theconversion event correlates the conversion event with the spot thataired on the offline medium.

In some cases, a time gap may exist between when a UV visits a websiteand when a conversion of that UV occurs. Accordingly, in someembodiments, the method may further comprise determining whether a timegap exists between a timestamp associated with a start of a UV sessionassociated with a UV and a timestamp for a conversion of the UV.Responsive to finding the time gap, the attribution system is operableto compare the time gap with a predetermined threshold (e.g., five days,a week, three months, etc.) and, responsive to the time gap exceedingthe predetermined threshold, the attribution system is operable todetermine an appropriate session timestamp for the conversion event. Forinstance, the attribution system may determine a UV session associatedwith the UV that is temporally the closest to the timestamp for theconversion of the UV and utilizes a timestamp of that UV session as thesession timestamp for the conversion event.

In some embodiments, once all the conversions are correlated to theattribution window of interest (also referred to herein as a cohort),the method may further comprise determining an overall conversion rateof UVs to the online medium in the attribution window and determiningthe attribution by the spot that aired on the offline medium to theoverall conversion rate of UVs to the online medium in the attributionwindow. In some embodiments, this determination can include examining,on a minute by minute basis, the overall conversion rate relative to aspecific lift in the UVs to the online medium that is caused by theoffline medium in the attribution window of time.

In some embodiments, this determination can include isolating thespecific lift caused by the offline medium in the attribution window oftime from a total lift in the UVs to the online medium. The total liftcan be determined using a UV baseline. The UV baseline can beestablished using the chronologically merged online and offline data. Onthe specific lift is isolated, an alpha factor can be determined. Thisalpha factor represents a ratio between a conversion rate of UVs drivento the online medium by the spot that aired on the offline medium and aconversion rate of UVs to the online medium not driven by the spot thataired on the offline medium. The alpha factor is utilized by theattribution system to adjust the overall conversion rate of UVs to theonline medium in the attribution window of time. This adjustmentreflects the offline medium's attribution to the online medium'sconversions.

Results generated by the attribution analyzer can be utilized by thevisualizer to generate a visualization for presentation on a clientdevice. The visualization graphically illustrates, through a userinterface, the attribution by the spot that aired on the offline mediumto the overall conversion rate of UVs to the online medium in theattribution window of time. The visualization can show how the spotperforms relative to time and other spot by different metrics (e.g., bynetwork, by proportional conversion rate, by customer acquisition cost,etc.).

The visualizer of the attribution system can generate variousvisualizations that can offer rich, contextual information aggregated byand at the attribution system. For instance, the visualizer can generategraphs based on viewership of the television network and on the UVs ofthe website, order data points of the graphs by time, and overlay thesegraphs chronologically. The visualization of these graphs can beinteractive, allowing a user to drill down to a cohort and reviewoverlaid data points (e.g., UVs, baseline, networks, etc.) on a minuteby minute basis.

One embodiment may comprise a system having a processor and a memory andconfigured to implement the method disclosed herein. One embodiment maycomprise a computer program product that comprises a non-transitorycomputer-readable storage medium which stores computer instructions thatare executable by a processor to perform the method disclosed herein.Numerous other embodiments are also possible.

These, and other, aspects of the disclosure will be better appreciatedand understood when considered in conjunction with the followingdescription and the accompanying drawings. It should be understood,however, that the following description, while indicating variousembodiments of the disclosure and numerous specific details thereof, isgiven by way of illustration and not of limitation. Many substitutions,modifications, additions and/or rearrangements may be made within thescope of the disclosure without departing from the spirit thereof, andthe disclosure includes all such substitutions, modifications, additionsand/or rearrangements.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings accompanying and forming part of this specification areincluded to depict certain aspects of the disclosure. It should be notedthat the features illustrated in the drawings are not necessarily drawnto scale. A more complete understanding of the disclosure and theadvantages thereof may be acquired by referring to the followingdescription, taken in conjunction with the accompanying drawings inwhich like reference numbers indicate like features.

FIG. 1 depicts a diagrammatic representation of an attribution systemthat bridges a data gap between the online world and the offline worldaccording to some embodiments.

FIG. 2 depicts a diagrammatic representation of an attribution servercommunicatively connected to a variety of online and offline datasources according to some embodiments.

FIG. 3A is a flow chart illustrating a method for attributing TVconversions according to some embodiments.

FIG. 3B is a flow chart illustrating additional details of the method ofFIG. 3A according to some embodiments.

FIG. 4 depicts a diagrammatic representation of a user interface showinga unique visualization that overlays offline data and online datachronologically according to some embodiments.

FIG. 5 illustrates diagrammatically how an attribution server candetermine an appropriate session timestamp for a conversion eventaccording to some embodiments.

FIG. 6 is a plot diagram illustrating an example of a relationshipbetween an online medium's conversion rate and a percentage of uniquevisitors driven to the online medium by an offline medium according tosome embodiments.

FIG. 7 depicts a diagrammatic representation of an example userinterface showing a visualization of results generated by an attributionserver, the results including a percentage of an online medium'sconversion rate that can be quantifiably attributed to an offline mediumaccording to some embodiments.

FIG. 8 depicts a diagrammatic representation of another example userinterface showing a visualization of results generated by an attributionserver, the results including a customer acquisition cost that can bequantifiably attributed to an offline medium according to someembodiments.

FIG. 9 depicts a diagrammatic representation of a data processing systemfor implementing a system according to some embodiments.

DETAILED DESCRIPTION

The disclosure and various features and advantageous details thereof areexplained more fully with reference to the exemplary, and thereforenon-limiting, embodiments illustrated in the accompanying drawings anddetailed in the following description. It should be understood, however,that the detailed description and the specific examples, whileindicating the preferred embodiments, are given by way of illustrationonly and not by way of limitation. Descriptions of known programmingtechniques, computer software, hardware, operating platforms andprotocols may be omitted so as not to unnecessarily obscure thedisclosure in detail. Various substitutions, modifications, additionsand/or rearrangements within the spirit and/or scope of the underlyinginventive concept will become apparent to those skilled in the art fromthis disclosure.

As alluded to above, while the Nielsen's ratings system can provide somequantified measures of audience response to TV programs, the Nielsenratings do not measure conversion rates for TV commercials. This is, inpart, because there is a natural distinction between different types ofmedia (which, collectively, refers to the main means of masscommunication such as broadcasting, publishing, and the Internet):online (e.g., search engine marketing) and offline (e.g., linear TV).

Online media is effective when consumers are already accessing theInternet through a website or an application (e.g., a mobileapplication, an email application, etc.). When a user is attracted to aproduct and visits a website for the product or service, the website canbe seen as an online advertising channel and there is a sessionassociated with that online advertising channel. The user's interactionswith the website and activities on the website (which can also bereferred to as surfing the website) can be logged or otherwise tracked(e.g., by a web server hosting the website) and associated with thesession. This tracking can begin when the user arrives at the website.When the user arrives at the website from an email or another website(which can be referred to as a referral source) through a special link(e.g., a universal resource identifier or locator embedded in the bodyof the email or a page or pages of the referral site as a trackingpixel), information about the referral source is recorded as well. Atracking pixel is an HyperText Markup Language (HTML) code snippet knownto those skilled in the art and thus is not further described herein.

Because the user's movements and clicks with respect to the website canbe monitored throughout the session, determining whether the sessionresults in a conversion event is a relatively straightforward process.This process can entail examining the session data, extractinginformation about the referral source, and directly attributing (givingcredit where credit is due) the conversion event to the referral source.A conversion event, in this case, can be any configurable goal of theonline medium at issue (e.g., in this example, the website), forinstance, an email registration, a purchase, a download, a signature, asubscription, etc.

This kind of direct attribution is not possible with offline media. Forinstance, it is not possible to directly attribute a user's conversionevent at a website to an offline event such as a spot airing on anetwork through linear TV. This is because offline media aims to driveconsumers first to the Internet and then to a website or applicationassociated with a particular product or service. Further, unlike onlinemedia, there is neither session tracking nor a direct relationshipbetween an offline medium and the desired result (e.g., a conversionevent). Thus, suppose a spot that aired on a network encouraged itsviewer to visit a website or download an application, it can beextremely difficult to measure the impact of that spot and quantifiablyattribute any website visit or conversion event to the particular spot.As illustrated in FIG. 1, this natural distinction between the differenttypes of media creates a data gap between the digital world and theanalog world.

From the perspective of a website, although the general trafficcontribution from a network (which represents an offline medium) to thewebsite (which represents an online medium) can be assessed from theimmediate lift approximation, there is no digital evidence forattributing specific online conversion events at the website to thenetwork. For example, after a spot aired on the network at a certaintime, a web server hosting the website mentioned in the spot can detectan immediate visitor lift at the website. However, the web server has noway of knowing which user visits the website because of the spot. Thereis not a one-to-one relationship between a website visitor and TVviewership. Further, there is not a session variable or tag that the webserver can use to track whether a user's visit comes “from TV.” Thismeans that if the user's visit to the website results in a conversionevent, it is not possible to directly attribute this conversion event tothe network. This creates a challenge to estimate TV contribution towebsite conversions.

To overcome this challenge, embodiments disclosed herein provide newsystems and methods for attributing TV conversions—conversion eventsoriginating from spots aired on an offline medium (e.g., linear TV). Theattribution technology disclosed herein can be useful for measuringeffectiveness of linear TV spots.

As exemplified in FIG. 1, in some embodiments, the attributiontechnology disclosed herein can be realized in attribution system 100that bridges the digital world and the analog world. Attribution system100 can run on one or more server machines and can include attributionanalyzer 110 and visualizer 120.

In some embodiments, attribution system 100 can aggregate online datafrom online media (e.g., clickstream data collected by a server hostingan online medium) and offline data from offline media (e.g., spot airingdata associated spots aired on a network) in data store 130. Generally,a clickstream, also referred to as a click path, refers to a record of auser's activities on the Internet. A clickstream can include a sequenceof hyperlinks that the user has followed within a website and/or acrossmultiple websites and can include timestamps documenting the user'schronological movements and web surfing activities. Clickstream data caninclude multiple clickstreams of multiple users in the online world.

In some embodiments, attribution system 100 does not directly collectinformation about viewers 109 a . . . 109 n in the offline world.However, viewers 109 a . . . 109 n may include users of the onlineworld. Encouraged by a spot that they saw while watching a TV show on anoffline medium, some viewers may visit an online medium (e.g., awebsite, an application, etc.) using their devices (e.g., user device101 a, 101 n, etc.).

In some embodiments, attribution system 100 can, through attributionanalyzer 110 with the data thus aggregated from online and offlinemedia, compute the attribution of TV spots that aired in an attributionwindow through an offline medium (e.g., a network) to conversion eventsthat took place during the attribution window through an online medium(e.g., a website, an application, etc.). In some embodiments, visualizer120 is operable to prepare a user interface or dashboard using theoutcomes from attribution analyzer 110 (e.g., event metrics, spotperformance measures, etc.) for presentation on client devices 180 a . .. 180 n communicatively connected to attribution system 100.

FIG. 2 shows examples of disparate online and offline data sources anddata types that can be aggregated by attribution server 200 which, inone embodiment, can implement attribution system 100 shown in FIG. 1. Insome embodiments, outputs 230 that can be generated by attributionserver 200 can include TV conversion attribution to the overallconversion rate. In some embodiments, outputs 230 can be visualized andpresented through user interface (UI) or dashboard 250 to showperformance measurements of TV conversion attribution by differentmetrics (e.g., TV conversion attribution by network, spot cost, etc.).These aspects are further explained below.

FIG. 3A is a flow chart illustrating an example of a TV conversionattribution method. In some embodiments, an attribution serverimplementing method 300 may aggregate, receive, and/or obtain onlinedata and offline data from various data sources, such as those shown inFIG. 2 (301). As illustrated in FIG. 2, offline data can be obtainedfrom and/or provided by TV networks. Prelogs and postlogs are TV networkairing logs. “Prelog” refers to the planned schedule of TV spotpurchases for a client of an attribution system (e.g., attributionsystem 100) where the attribution server operates. Prelog containsinformation identifying such TV spots by network (with or withoutlocation information), date, and time in advance of the airing.“Postlog” refers to the actual times when spots aired on TV networks.Rates are how much the TV networks charge for commercial spots placed atvarious times of the day. Likewise, spot airing data, program schedules,and program demographics can be obtained or provided by TV networksand/or media agencies.

Online data can be obtained from and/or provided by digital dataproviders. These can include clickstream data from a data analyticsprovider which can include the number of unique visitors (UVs) for awebsite, the number of applications (apps) downloaded from an electronicsite or store, the number of purchases made on a website or app, etc.Online data can be collected from a variety of sources, includingwebsites, digital devices, consumer electronics, etc.

The offline data and the online data aggregated by and at theattribution server can be processed so that they are merged temporallyto provide a minute-by-minute account of what is occurring in both theoffline world and the online world (303). In some embodiments, theattribution server can generate a unique visualization that overlays theoffline data and the online data chronologically, as illustrated in FIG.4.

FIG. 4 depicts a diagrammatic representation of UI 400 showing that agraph generated based on TV viewership (which represents the offlinedata) can be overlaid on top of or under another graph generated basedon UVs of a website (which represents the online data) chronologically(e.g., on a minute by minute basis). In this example, the offline datagraph shows spot airings and corresponding ad costs across multiplenetworks (e.g., Network1, Network2, Network3, etc.) during a time period(e.g., 8 AM˜11 PM) and the online data graph shows jumps in the numberof UVs to the website relative to the website's baseline over the sametime period. UI 400 can be generated by a visualization functionality ofan attribution server disclosed herein (e.g., visualizer 120,attribution server 200, etc.) and presented to a user on the user'sdevice (e.g., client device 180 a, . . . , 180 n) over the Internet.

Referring to FIG. 3A, the merged data can be used to establish abaseline of website visitor traffic (305). There can be many ways toestablish such a baseline. For example, a website visitor trafficbaseline can be established by taking a moving average of UVs to awebsite throughout the day and/or the week and excluding UVs thatclearly do not come because of any TV spots (e.g., UVs that came to thewebsite through a link in an email, through a referring website, etc.).This is referred to as a UV baseline and is particular for TV conversionattribution computation (because it excludes non-TV influences). The UVbaseline essentially plots, over the course of a timespan, what isconsidered normal website visitor traffic to a website relative to TVspots, as reflected in the number of UVs to the website. A suitablebaselining technology can be found in U.S. Provisional Application No.62/776,583, filed on Dec. 7, 2018, entitled “SELF-CONSISTENT INCEPTIONARCHITECTURE FOR EFFICIENT BASELINING MEDIA CREATIVES,” which is fullyincorporated by reference herein.

Different TV spots may contain different messages to TV viewers. Forinstance, while some TV spots may be directed to a physical product,some TV spots may be directed to an online service, a new applicationinstall, an email registration, a game download, a signature for anonline petition, etc. For the purpose of illustration, suppose a TV spotcalls for viewers to take a certain action through a particular website(e.g., visit a website, buy a product or service through the website,download and/or install an application from the website, sign a petitionat the website, etc.), a “conversion” event (which is also referred toherein as a “conversion”) occurs when a UV to the particular websitetakes that action (e.g., the UV visited the website, the UV purchasedthe product or service through the website, the UV downloaded and/orinstalled the application through the website, the UV signed a petitionat the website, etc.). Such conversions can be considered as aconversion lift by the TV spot.

“Lift” is a quality metric for measuring the performance of a spot inthe context of a particular type of campaign. In this case, since themerged data contains the minute-by-minute cohort (i.e., all the UVs tothe website during the same minute), the attribution server cancalculate a conversion rate based on the cohort and determine thevisitor lift relative to the UV baseline (307). In statistics, a cohortrefers to a group of subjects or items that share a definingcharacteristic. Because cohort data is usually tied to a specific timeperiod, it is considered more accurate. In some embodiments, cohorts canbe found by examining every minute in a day for a number of days duringwhich offline data and online data have been aggregated and merged withregard to a particular website. An overall conversion rate is computedfor each minute (discussed below). A goal here is to isolate theconversion rate during a particular time window (which is referred toherein as an attribution window) for a particular TV spot aired on aparticular network.

Because a cohort based on which the conversion rate is calculated mayinclude both linear TV viewers and non-linear-TV viewers, the initialresult from this calculation can be skewed. For example, suppose a spotaired on a TV network at 6:20 PM and a lift (an increase in theconversion rate) occurred shortly after 6:20 PM, it is possible that thelift can be attributed to the spot that aired at 6:20 PM. However, it isunclear how much of that lift can actually be attributed to the spotthat aired at 6:20 PM.

One way to eliminate this skew and quantify the attribution of such alift to TV conversion is to examine session timestamps, correlatesession timestamps to the spot airing data, and assign a timestamp to aconversion event that occurred on a website (311). As illustrated inFIG. 3B, in some embodiments, this examination can be triggered when theattribution server detects or otherwise determines that a conversionevent has occurred with respect to a UV (313). In response, theattribution server is operable to traverse a clickstream associated withthe UV in reverse chronologically. This can be done by examining everyminute of the UV's visit to the website from the time the conversionevent took place to when the UV first started the session that led tothe conversion. This session is identified as the “active session”(315).

The timestamp of this active session is obtained (317) and assigned tothe conversion event. This conversion-triggered examination andassignment of session timestamp means that the attribution server doesnot need to actively track and store all UV sessions at all times.Rather, the attribution server only needs to know when and what keyevents happened at a website (e.g., a sale, a new email registration, anew UV, etc.) and, after a conversion by a UV occurs, review the UV'sclickstream and find the appropriate session timestamp. For example, aUV first visits a website at 1:15 PM, clicks on a menu at 1:17 PM, andpurchased an item through the website at 1:21 PM. Here, the timestampfor the conversion event is 1:21 PM and the timestamp for the activesession is 1:15 PM. The attribution server reviews the UV's clickstream,finds the timestamp for the active session “1:15 PM,” and assigns it tothe conversion event.

The TV conversion attribution technology disclosed herein utilizes thetimestamp associated with the active session instead of the actual timewhen the conversion event took place because the timestamp associatedwith the active session is the closest in time that the attributionserver can associate the online conversion event with an offline spotairing event (i.e., when a TV conversion occurs). That is, theattribution server is concerned with correlating the timing of a UVshowing up at an online medium (which led to the UV ultimately takingthe desired action through the online medium) to the timing of a TV spotairing through an offline medium (which conveyed a message of taking thedesired action through the online medium). This correlation enables theattribution server to more accurately associate the conversion event forthe UV (not based on when the conversion event took place, but based onwhen the UV started the process which lead to the conversion event) witha window of time when a spot aired on a TV network. In some embodiments,the term “network” can include all stations affiliated with a particularnetwork. In some embodiments, the term “network” can refer to a set ofstations representing the network, for instance, in a geographicallocale, region, or time zone.

The conversion event timestamp assignment process described above isirrespective of the spot airing date/time. The assigned timestamps forconversion events taking place online can then be correlated to whenspots aired offline. In some embodiments, the attribution server canstore this correlation in a database.

There are other ways to determine what conversion events at an onlinemedium could be attributed to a spot that aired through an offlinemedium may begin with the examination of UV sessions initiated at theonline medium within an attribution window associated with the spot thataired through the offline medium. For instance, following the aboveexample in which a spot aired on a TV network at 6:20 PM, theattribution server is operable to examine UV sessions initiated at thewebsite within a five minute window starting at 6:20 PM (e.g., each offive minutes at 6:20 PM, 6:21 PM, 6:23 PM, 6:24 PM, and 6:25 PM) anddetermine whether a conversion ultimately occurs in a UV session. If so,the attribution server is operable to assign the start of the UV session(i.e., the timestamp for the active session) to the conversion. In turn,the conversion is correlated to the spot associated with the attributionwindow.

As a non-limiting example, suppose the spot aired on the TV network at6:20 PM on Day One with a message for viewers to donate to a charity. AUV initiated a session with the charity's website at 6:25 PM on Day Oneand subsequently made a donation (e.g., a hour, a day, or even a weeklater). In this example, the attribution server assigns 6:25 PM on DayOne, when the UV initiated the session with the charity's website, asthe timestamp for the conversion event. Because the timestamp of 6:25 PMfalls within the five minute attribution window for the spot, theconversion event is correlated to the spot that aired on the network at6:20 PM.

In some cases, a conversion (which, in this example, is a donation madethrough the website) can take place in the same session with no timegap. This scenario is illustrated in FIG. 5 (e.g., scenario 500A). Insome cases, there could be a time gap between visits by the same UV tothe website. This scenario is also illustrated in FIG. 5 (e.g., scenario500B or scenario 500C).

As scenario 500B illustrates, if there is a time gap between when a UVfirst visits an online medium and when the UV takes a desired action(e.g., a user conversion) through that online medium, the attributionserver may use the earliest session timestamp as the timestamp for theuser conversion if the time gap is not more than a predeterminedthreshold (e.g., three months, six months, one year, etc.). As scenario500C illustrates, the attribution server may use the latest (active)session timestamp as the timestamp for a conversion event if the mostrecent session and the one before it has a temporal gap that is largerthan the predetermined threshold. That is, if there are multiplesessions, the attribution server may select an appropriate activesession based on the size of a temporal gap between the multiplesessions.

Now that each conversion event has an assigned timestamp, theattribution server may operate to determine conversion rates of UVs of awebsite during a time window defined relative to the spot airing time(321). The attribution server may keep a file (e.g., in data store 130)for every UV to a particular website (from the online data), documentingwhen UVs visit the website, when their sessions started, and when/if aconversion event took place. The attribution server can compare, on aminute by minute basis, user interactions with the website relative towhen a sport aired (e.g., at 6:25 PM). For example, using a fixed window(e.g., five minutes for this website), anything occurred during 6:22PM-6:27 PM, the attribution server can determine how UVs visited thewebsite in that window of time and how many conversion events have takenplace in that window of time. This gives an overall conversion rate inthe TV window (which includes both TV responders and non-TV responders).

As a non-limiting example, suppose there are a total of 100 UVs to thewebsite over a five-minute window and 10 of them made a purchase. Thisresults in a 10% conversion rate in general for that window. Thisoverall conversion rate can be defined as follows:

Conversion rate=number of conversions that occurred as a result of UVsthat started their session within a time window/number of UVs within thetime window. Or

${{{conversion}\mspace{14mu}{{rate}({tv})}} = {{r({tv})} = {\Sigma_{t}\frac{c}{n}}}},$

where c=# conversions in that minute,

n=# UVs in that minute,

t ∈ (set of all tv attribution windows)

This conversion rate covers all UVs to the website regardless of whatdrove them to the website. Since these UVs could include TV responderswho responded to a spot aired on a network (offline) and non-TVresponders who did not visit the website because of the spot, theattribution of airing the spot on TV to the overall conversion rate isunclear. Accordingly, in some embodiments, the attribution server isoperable to determine how much of this conversion rate can be attributedto TV (i.e., the conversion rate of TV responders) (331).

In some embodiments, a determination of TV attribution to a conversionrate can be made by isolating the lift caused by TV on a minute byminute basis, determining a new alpha factor (a), and adjusting theconversion rate using the alpha factor. In this disclosure, the alphafactor represents a ratio between the conversion rate of TV UVs and theconversion rate of non-TV UVs.

To understand the conversion rate of TV UVs, an example of a conversionflow that involves both the online world and the offline world may behelpful. Suppose John Doe is offline. John may see a TV spot whilewalking by a TV. The spot encourages viewers to sign a petition for agood cause and provides an URL of a website. Intrigued, John pulls outhis mobile device (e.g., a smart phone, a tablet, a laptop, etc.) anddirects a browser on his device to the URL of the website. Since Johnhas never visited the website before, John is a UV to the website. Afterbrowsing through the website, John decides to support the cause andsigns the petition. A server hosting or monitoring the website recordsthis new UV session and sends the clickstream data to the attributionserver. The attribution server determines an appropriate sessiontimestamp (which indicates the time when John first visits the website)for the conversion event (which, in this example, refers to John signingthe petition) and, based on the session timestamp, correlates theconversion event to a time window within which John saw the TV spot. Inthis conversion flow, John is considered a TV responder or a TV UV.

As discussed above, a lift can be computed and associated with eachminute the attribution server has data for a website (e.g., every minuteacross all the days and months running a campaign). Also assigned toeach minute is a percentage of lift (e.g., 0 to 100%) that came fromimmediate TV responders such as John Doe described above. Below is anexample of how this percentage of TV-attributed lift can be determined.

Suppose the attribution server has computed the overall conversion rate(i.e., the number of conversion events divided by the number of UVs perminute) as described above. FIG. 6 is a plot diagram which shows anexample scenario in which the overall conversion rate is 2% when thepercentage of TV users is 0 and, when the percentage of TV users is 100,the overall conversion rate is 1%. In this example, the TV users havehalf the conversion rate in general than the non-TV users. This ratio isrepresented by the alpha factor. The alpha factor can be determinedusing an ordinary least squares (OLS) regression technique as follows:

y=α ₀ +αX+ε,

where y=vector of conversion rates by minute and X=vector of % TV users(lift/UV) per minute.

The attribution server is operable to identify the subsequentattribution window after the spot aired for which credit will be givento that TV spot as having contributed to the incremental lift over theUV baseline. That is, the attribution server is operable to examine thepercentage of TV users for each of those minutes and solves what thatalpha factor is at 100% TV lift to 0 TV lift.

Once the alpha factor is determined, the conversion rate for TVresponders can be determined by multiplying the number of conversions bya factor lambda (A) representing the influence of those TV responders.This is reflected in the new equation below:

${{{conversion}\mspace{14mu}{{rate}({tv})}} = {{r({tv})} = {\Sigma_{t}\frac{\lambda_{t}c_{t}}{m_{t}}}}},{{{where}\mspace{14mu}\lambda} = \frac{\alpha\; p}{{\alpha\; p} + \left( {1 - p} \right)}},{m = {lift}},{and}$${p = {{{likelihood}\mspace{14mu}{of}\mspace{14mu}{user}\mspace{14mu}{being}\mspace{14mu}{from}\mspace{14mu}{tv}} = {\frac{lift}{UV}\mspace{14mu}{for}\mspace{14mu}{that}\mspace{14mu}{minute}}}},{\alpha = {{ratio}\mspace{14mu}{of}\mspace{14mu}{tv}\mspace{14mu}{conversion}\mspace{14mu}{rate}\mspace{14mu}{to}\mspace{14mu}{nontv}\mspace{14mu}{conversion}\mspace{14mu}{rate}}}$

By computing this equation, the attribution server can determine theconversion that can be attributed to TV responders (i.e., TV conversionattribution). That is, the attribution server is operable to examine thetotal conversion rate relative to a specific lift, multiplying the totalnumber of conversions by the lambda factor, which is the alpha factortimes p divided by the alpha factor times p plus one minus p. Putanother way, the conversions that were tracked and ascribed to those UVsin a TV window (a cohort) are adjusted by the proportion of TV viewerconversions responding in that window; i.e., proportion of conversionsassociated with TV viewer lift divided by the proportion of conversionsassociated with TV viewer lift plus the proportion of conversionsassociated with non-TV viewers. This is the conversion rate of immediateTV responders.

Essentially, the attribution server examines all occurrences of TV spotsand isolates the UVs that started their sessions within a TV attributionwindow. Then, the attribution server assigns conversions to those UVs.Generally, this approach can be described as follows:

Compute user-level fuzzy likelihoods: Identify likelihood, P(TV), ofeach user coming in from TV. As an example, the attribution server looksat the minute-by-minute cohorts—if a lift of 10 on total traffic thatminute of 100, then all users that minute are assigned 10% odds ofcoming from TV.

Increase accuracy of conversion estimates: The attribution serverexamines the conversion rate per cohort relative to the percentage liftper cohort, and creates an attribution model that leverages theconversion rate per cohort relative to the percentage lift per cohort aswell as the network. An objective here is to modify the approach fromone of assuming the conversion rate for each cohort is the samethroughout the cohort to one of adjusting for potential differences inconversion rate for TV responders vs non-TV responders within thecohort.

This invention provides a reasonable prediction to fill the data gapbetween the online media and the offline media. Streaming TV services donot need to do these processes and perform these calculations becausetiming information is readily available—when does a viewer watch whatprogram or spot is digitally recorded, as well as when that viewer thenvisits a website and how that viewer interacts with the website. Thedata gap problem is a unique problem to linear TV spots because there isno way to “tag” a TV responder when they visit a website—there is not aone-to-one relationship between an offline source (e.g., linear TV) andthe conversion rate for the website.

As a non-limiting example, suppose a TV spot aired near 6:22 PM. In thefirst minute of that airing, a website records 360 UVs, with a baselineof 36. This yields a probability (p) of 0.9 (p=0.9)−90% of UVs visitingthe website in that minute would have been projected as coming fromwatching a TV ad. Assuming that the website ultimately has 18conversions associated with those UVs who visited the website duringthat minute, with an alpha factor of 0.5, this would yield an equationof:

$\lambda = {\frac{\alpha\; p}{{\alpha\; p} + \left( {1 - p} \right)} = {\frac{0.5*0.9}{{0.5*0.9} + 0.1} = {{{.45}\text{/}{.55}} = 0.818}}}$

In turn, this yields an adjusted TV conversion rate for that minute, t,of:

${{conversion}\mspace{14mu}{{rate}({tv})}} = {{r({tv})} = {\frac{\lambda\; c}{m} = {\frac{0.818*18}{\left( {360 - 36} \right)} = 0.0454}}}$

This conversion rate reflects the attribution of that TV spot to theoverall conversions at that website during the attribution window.

The computed TV conversion attribution can be visualized and presentedon various client devices (e.g., client devices 180 a, . . . 180 n). Asillustrated in FIGS. 7 and 8, TV can be attributed to a website's UVconversion rate over time in view of different metrics. FIG. 7 shows anexample of a percentage of TV attribution to an overall UV conversionrate over time with respect to purchases made by UVs to the website.FIG. 8 shows an example of spot cost-per-UV conversion (customeracquisition cost) over time. These visualizations can be generated andpresented to various client devices by a visualizer (e.g., visualizer120).

FIG. 9 depicts a diagrammatic representation of a data processing systemfor implementing a system for processing messages. As shown in FIG. 9,data processing system 900 may include one or more central processingunits (CPU) or processors 901 coupled to one or more user input/output(I/O) devices 902 and memory devices 903. Examples of I/O devices 909may include, but are not limited to, keyboards, displays, monitors,touch screens, printers, electronic pointing devices such as mice,trackballs, styluses, touch pads, or the like. Examples of memorydevices 903 may include, but are not limited to, hard drives (HDs),magnetic disk drives, optical disk drives, magnetic cassettes, tapedrives, flash memory cards, random access memories (RAMs), read-onlymemories (ROMs), smart cards, etc. Data processing system 900 can becoupled to display 906, information device 907 and various peripheraldevices (not shown), such as printers, plotters, speakers, etc. throughI/O devices 902. Data processing system 900 may also be coupled toexternal computers or other devices through network interface 904,wireless transceiver 905, or other means that is coupled to a networksuch as a local area network (LAN), wide area network (WAN), or theInternet.

Those skilled in the relevant art will appreciate that the invention canbe implemented or practiced with other computer system configurations,including without limitation multi-processor systems, network devices,mini-computers, mainframe computers, data processors, and the like. Theinvention can be embodied in a computer or data processor that isspecifically programmed, configured, or constructed to perform thefunctions described in detail herein. The invention can also be employedin distributed computing environments, where tasks or modules areperformed by remote processing devices, which are linked through acommunications network such as a LAN, WAN, and/or the Internet. In adistributed computing environment, program modules or subroutines may belocated in both local and remote memory storage devices. These programmodules or subroutines may, for example, be stored or distributed oncomputer-readable media, including magnetic and optically readable andremovable computer discs, stored as firmware in chips, as well asdistributed electronically over the Internet or over other networks(including wireless networks). Example chips may include ElectricallyErasable Programmable Read-Only Memory (EEPROM) chips. Embodimentsdiscussed herein can be implemented in suitable instructions that mayreside on a non-transitory computer readable medium, hardware circuitryor the like, or any combination and that may be translatable by one ormore server machines. Examples of a non-transitory computer readablemedium are provided below in this disclosure.

ROM, RAM, and HD are computer memories for storing computer-executableinstructions executable by the CPU or capable of being compiled orinterpreted to be executable by the CPU. Suitable computer-executableinstructions may reside on a computer readable medium (e.g., ROM, RAM,and/or HD), hardware circuitry or the like, or any combination thereof.Within this disclosure, the term “computer readable medium” is notlimited to ROM, RAM, and HD and can include any type of data storagemedium that can be read by a processor. Examples of computer-readablestorage media can include, but are not limited to, volatile andnon-volatile computer memories and storage devices such as random accessmemories, read-only memories, hard drives, data cartridges, directaccess storage device arrays, magnetic tapes, floppy diskettes, flashmemory drives, optical data storage devices, compact-disc read-onlymemories, and other appropriate computer memories and data storagedevices. Thus, a computer-readable medium may refer to a data cartridge,a data backup magnetic tape, a floppy diskette, a flash memory drive, anoptical data storage drive, a CD-ROM, ROM, RAM, HD, or the like.

The processes described herein may be implemented in suitablecomputer-executable instructions that may reside on a computer readablemedium (for example, a disk, CD-ROM, a memory, etc.). Alternatively oradditionally, the computer-executable instructions may be stored assoftware code components on a direct access storage device array,magnetic tape, floppy diskette, optical storage device, or otherappropriate computer-readable medium or storage device.

Any suitable programming language can be used to implement the routines,methods, or programs of embodiments of the invention described herein,including Python. Other software/hardware/network architectures may beused. For example, the functions of the disclosed embodiments may beimplemented on one computer or shared/distributed among two or morecomputers in or across a network. Communications between computersimplementing embodiments can be accomplished using any electronic,optical, radio frequency signals, or other suitable methods and tools ofcommunication in compliance with known network protocols.

Different programming techniques can be employed such as procedural orobject oriented. Any particular routine can execute on a single computerprocessing device or multiple computer processing devices, a singlecomputer processor or multiple computer processors. Data may be storedin a single storage medium or distributed through multiple storagemediums, and may reside in a single database or multiple databases (orother data storage techniques). Although the steps, operations, orcomputations may be presented in a specific order, this order may bechanged in different embodiments. In some embodiments, to the extentmultiple steps are shown as sequential in this specification, somecombination of such steps in alternative embodiments may be performed atthe same time. The sequence of operations described herein can beinterrupted, suspended, or otherwise controlled by another process, suchas an operating system, kernel, etc. The routines can operate in anoperating system environment or as stand-alone routines. Functions,routines, methods, steps, and operations described herein can beperformed in hardware, software, firmware, or any combination thereof.

Embodiments described herein can be implemented in the form of controllogic in software or hardware or a combination of both. The controllogic may be stored in an information storage medium, such as acomputer-readable medium, as a plurality of instructions adapted todirect an information processing device to perform a set of stepsdisclosed in the various embodiments. Based on the disclosure andteachings provided herein, a person of ordinary skill in the art willappreciate other ways and/or methods to implement the invention.

It is also within the spirit and scope of the invention to implement insoftware programming or code any of the steps, operations, methods,routines or portions thereof described herein, where such softwareprogramming or code can be stored in a computer-readable medium and canbe operated on by a processor to permit a computer to perform any of thesteps, operations, methods, routines or portions thereof describedherein. The invention may be implemented by using software programmingor code in one or more digital computers, by using application specificintegrated circuits, programmable logic devices, field programmable gatearrays, optical, chemical, biological, quantum or nanoengineeredsystems, components and mechanisms may be used. The functions of theinvention can be achieved in many ways. For example, distributed ornetworked systems, components, and circuits can be used. In anotherexample, communication or transfer (or otherwise moving from one placeto another) of data may be wired, wireless, or by any other means.

A “computer-readable medium” may be any medium that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, system, ordevice. The computer readable medium can be, by way of example only butnot by limitation, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, system, device,propagation medium, or computer memory. Such computer-readable mediumshall be machine readable and include software programming or code thatcan be human readable (e.g., source code) or machine readable (e.g.,object code). Examples of non-transitory computer-readable media caninclude random access memories, read-only memories, hard drives, datacartridges, magnetic tapes, floppy diskettes, flash memory drives,optical data storage devices, compact-disc read-only memories, and otherappropriate computer memories and data storage devices. In anillustrative embodiment, some or all of the software components mayreside on a single server computer or on any combination of separateserver computers. As one skilled in the art can appreciate, a computerprogram product implementing an embodiment disclosed herein may compriseone or more non-transitory computer readable media storing computerinstructions translatable by one or more processors in a computingenvironment.

A “processor” includes any, hardware system, mechanism or component thatprocesses data, signals or other information. A processor can include asystem with a central processing unit, multiple processing units,dedicated circuitry for achieving functionality, or other systems.Processing need not be limited to a geographic location, or havetemporal limitations. For example, a processor can perform its functionsin “real-time,” “offline,” in a “batch mode,” etc. Portions ofprocessing can be performed at different times and at differentlocations, by different (or the same) processing systems.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having,” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,product, article, or apparatus that comprises a list of elements is notnecessarily limited only those elements but may include other elementsnot expressly listed or inherent to such process, product, article, orapparatus.

Furthermore, the term “or” as used herein is generally intended to mean“and/or” unless otherwise indicated. For example, a condition A or B issatisfied by any one of the following: A is true (or present) and B isfalse (or not present), A is false (or not present) and B is true (orpresent), and both A and B are true (or present). As used herein, a termpreceded by “a” or “an” (and “the” when antecedent basis is “a” or “an”)includes both singular and plural of such term (i.e., that the reference“a” or “an” clearly indicates only the singular or only the plural).Also, as used in the description herein, the meaning of “in” includes“in” and “on” unless the context clearly dictates otherwise. The scopeof the present disclosure should be determined by the following claimsand their legal equivalents.

What is claimed is:
 1. A method, comprising: merging, by a computer on aminute-by-minute basis, online data and offline data from disparate datasources to produce merged data that accounts for what occurred onlineand offline chronologically; determining, by the computer, uniquevisitor (UV) sessions initiated at an online medium within anattribution window of time for a spot that aired on an offline medium;determining, by the computer for each UV session initiated at the onlinemedium within the attribution window of time for the spot that aired onthe offline medium, whether a conversion event occurred at the onlinemedium; determining, by the computer based on conversion events occurredat the online medium, an overall conversion rate of UVs to the onlinemedium in the attribution window of time; computing, by the computer ona minute-by-minute basis, user-level fuzzy likelihoods of UVs initiatingUV sessions at the online medium within the attribution window of timebecause of the spot that aired on the offline medium; determining, bythe computer utilizing the user-level fuzzy likelihoods thus computed, aconversion rate that reflects an attribution of the spot that aired onthe offline medium to the overall conversion rate of the UVs to theonline medium in the attribution window of time; and generating, by thecomputer for presentation on a user device, a visualization of theattribution of the spot that aired on the offline medium to the overallconversion rate of the UVs to the online medium in the attributionwindow of time.
 2. The method according to claim 1, wherein computingthe user-level fuzzy likelihoods of UVs initiating UV sessions at theonline medium within the attribution window of time because of the spotthat aired on the offline medium comprises: examining the merged data ona minute-by-minute basis; determining, on a minute-by-minute basis, alift on total traffic to the online medium for a respective minute inthe attribution window of time; and assigning a percentage correspondingto the lift as a user-level fuzzy likelihood that UVs to the onlinemedium in the respective minute are attributable to the spot that airedon the offline medium.
 3. The method according to claim 1, wherein theoverall conversion rate of UVs to the online medium in the attributionwindow of time is determined on a minute-by-minute basis and comprisesdividing a number of conversion events by a number of UVs to the onlinemedium per minute.
 4. The method according to claim 1, furthercomprising: examining a plurality of occurrences of spots that aired onthe offline medium; determining an attribution window for eachrespective spot that aired on the offline medium; isolating UVs thatstarted sessions within each respective attribution window; and for eachrespective attribution window, determining an overall conversion ratefor UVs that started sessions within each respective attribution window.5. The method according to claim 4, further comprising: examining theoverall conversion rate relative to an incremental lift to total trafficto the online medium within a respective attribution window; andadjusting the overall conversion rate for the UVs that started sessionswithin the respective attribution window based at least in part on aratio between a conversion rate of UVs driven to the online medium by arespective spot that aired on the offline medium and a conversion rateof UVs to the online medium not driven by the respective spot that airedon the offline medium.
 6. The method according to claim 1, furthercomprising: determining an alpha factor that represents a ratio betweena conversion rate of UVs driven to the online medium by the spot thataired on the offline medium and a conversion rate of UVs to the onlinemedium not driven by the spot that aired on the offline medium; andadjusting the overall conversion rate of the UVs to the online medium inthe attribution window of time using the alpha factor.
 7. The methodaccording to claim 1, further comprising: determining the attributionwindow of time after the spot aired on the offline medium.
 8. A system,comprising: a processor; a non-transitory computer-readable medium; andstored instructions translatable by the processor for: merging, on aminute-by-minute basis, online data and offline data from disparate datasources to produce merged data that accounts for what occurred onlineand offline chronologically; determining unique visitor (UV) sessionsinitiated at an online medium within an attribution window of time for aspot that aired on an offline medium; determining, for each UV sessioninitiated at the online medium within the attribution window of time forthe spot that aired on the offline medium, whether a conversion eventoccurred at the online medium; determining, based on conversion eventsoccurred at the online medium, an overall conversion rate of UVs to theonline medium in the attribution window of time; computing, on aminute-by-minute basis, user-level fuzzy likelihoods of UVs initiatingUV sessions at the online medium within the attribution window of timebecause of the spot that aired on the offline medium; determining,utilizing the user-level fuzzy likelihoods thus computed, a conversionrate that reflects an attribution of the spot that aired on the offlinemedium to the overall conversion rate of the UVs to the online medium inthe attribution window of time; and generating, for presentation on auser device, a visualization of the attribution of the spot that airedon the offline medium to the overall conversion rate of the UVs to theonline medium in the attribution window of time.
 9. The system of claim8, wherein computing the user-level fuzzy likelihoods of UVs initiatingUV sessions at the online medium within the attribution window of timebecause of the spot that aired on the offline medium comprises:examining the merged data on a minute-by-minute basis; determining, on aminute-by-minute basis, a lift on total traffic to the online medium fora respective minute in the attribution window of time; and assigning apercentage corresponding to the lift as a user-level fuzzy likelihoodthat UVs to the online medium in the respective minute are attributableto the spot that aired on the offline medium.
 10. The system of claim 8,wherein the overall conversion rate of UVs to the online medium in theattribution window of time is determined on a minute-by-minute basis andcomprises dividing a number of conversion events by a number of UVs tothe online medium per minute.
 11. The system of claim 8, wherein thestored instructions are further translatable by the processor for:examining a plurality of occurrences of spots that aired on the offlinemedium; determining an attribution window for each respective spot thataired on the offline medium; isolating UVs that started sessions withineach respective attribution window; and for each respective attributionwindow, determining an overall conversion rate for UVs that startedsessions within each respective attribution window.
 12. The system ofclaim 11, wherein the stored instructions are further translatable bythe processor for: examining the overall conversion rate relative to anincremental lift to total traffic to the online medium within arespective attribution window; and adjusting the overall conversion ratefor the UVs that started sessions within the respective attributionwindow based at least in part on a ratio between a conversion rate ofUVs driven to the online medium by a respective spot that aired on theoffline medium and a conversion rate of UVs to the online medium notdriven by the respective spot that aired on the offline medium.
 13. Thesystem of claim 8, wherein the stored instructions are furthertranslatable by the processor for: determining an alpha factor thatrepresents a ratio between a conversion rate of UVs driven to the onlinemedium by the spot that aired on the offline medium and a conversionrate of UVs to the online medium not driven by the spot that aired onthe offline medium; and adjusting the overall conversion rate of the UVsto the online medium in the attribution window of time using the alphafactor.
 14. The system of claim 8, wherein the stored instructions arefurther translatable by the processor for: determining the attributionwindow of time after the spot aired on the offline medium.
 15. Acomputer program product comprising a non-transitory computer-readablemedium storing instructions translatable by a processor for: merging, ona minute-by-minute basis, online data and offline data from disparatedata sources to produce merged data that accounts for what occurredonline and offline chronologically; determining unique visitor (UV)sessions initiated at an online medium within an attribution window oftime for a spot that aired on an offline medium; determining, for eachUV session initiated at the online medium within the attribution windowof time for the spot that aired on the offline medium, whether aconversion event occurred at the online medium; determining, based onconversion events occurred at the online medium, an overall conversionrate of UVs to the online medium in the attribution window of time;computing, on a minute-by-minute basis, user-level fuzzy likelihoods ofUVs initiating UV sessions at the online medium within the attributionwindow of time because of the spot that aired on the offline medium;determining, utilizing the user-level fuzzy likelihoods thus computed, aconversion rate that reflects an attribution of the spot that aired onthe offline medium to the overall conversion rate of the UVs to theonline medium in the attribution window of time; and generating, forpresentation on a user device, a visualization of the attribution of thespot that aired on the offline medium to the overall conversion rate ofthe UVs to the online medium in the attribution window of time.
 16. Thecomputer program product of claim 15, wherein computing the user-levelfuzzy likelihoods of UVs initiating UV sessions at the online mediumwithin the attribution window of time because of the spot that aired onthe offline medium comprises: examining the merged data on aminute-by-minute basis; determining, on a minute-by-minute basis, a lifton total traffic to the online medium for a respective minute in theattribution window of time; and assigning a percentage corresponding tothe lift as a user-level fuzzy likelihood that UVs to the online mediumin the respective minute are attributable to the spot that aired on theoffline medium.
 17. The computer program product of claim 15, whereinthe overall conversion rate of UVs to the online medium in theattribution window of time is determined on a minute-by-minute basis andcomprises dividing a number of conversion events by a number of UVs tothe online medium per minute.
 18. The computer program product of claim15, wherein the instructions are further translatable by the processorfor: examining a plurality of occurrences of spots that aired on theoffline medium; determining an attribution window for each respectivespot that aired on the offline medium; isolating UVs that startedsessions within each respective attribution window; and for eachrespective attribution window, determining an overall conversion ratefor UVs that started sessions within each respective attribution window.19. The computer program product of claim 18, wherein the instructionsare further translatable by the processor for: examining the overallconversion rate relative to an incremental lift to total traffic to theonline medium within a respective attribution window; and adjusting theoverall conversion rate for the UVs that started sessions within therespective attribution window based at least in part on a ratio betweena conversion rate of UVs driven to the online medium by a respectivespot that aired on the offline medium and a conversion rate of UVs tothe online medium not driven by the respective spot that aired on theoffline medium.
 20. The computer program product of claim 15, whereinthe instructions are further translatable by the processor for:determining an alpha factor that represents a ratio between a conversionrate of UVs driven to the online medium by the spot that aired on theoffline medium and a conversion rate of UVs to the online medium notdriven by the spot that aired on the offline medium; and adjusting theoverall conversion rate of the UVs to the online medium in theattribution window of time using the alpha factor.